Overview

Dataset statistics

Number of variables25
Number of observations103904
Missing cells310
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.8 MiB
Average record size in memory200.0 B

Variable types

Numeric19
Categorical6

Alerts

Inflight wifi service is highly correlated with Ease of Online bookingHigh correlation
Ease of Online booking is highly correlated with Inflight wifi serviceHigh correlation
Food and drink is highly correlated with Seat comfort and 2 other fieldsHigh correlation
Seat comfort is highly correlated with Food and drink and 2 other fieldsHigh correlation
Inflight entertainment is highly correlated with Food and drink and 2 other fieldsHigh correlation
On-board service is highly correlated with Baggage handling and 1 other fieldsHigh correlation
Baggage handling is highly correlated with On-board service and 1 other fieldsHigh correlation
Inflight service is highly correlated with On-board service and 1 other fieldsHigh correlation
Cleanliness is highly correlated with Food and drink and 2 other fieldsHigh correlation
Departure Delay in Minutes is highly correlated with Arrival Delay in MinutesHigh correlation
Arrival Delay in Minutes is highly correlated with Departure Delay in MinutesHigh correlation
Inflight wifi service is highly correlated with Ease of Online bookingHigh correlation
Ease of Online booking is highly correlated with Inflight wifi serviceHigh correlation
Food and drink is highly correlated with Seat comfort and 2 other fieldsHigh correlation
Seat comfort is highly correlated with Food and drink and 2 other fieldsHigh correlation
Inflight entertainment is highly correlated with Food and drink and 2 other fieldsHigh correlation
On-board service is highly correlated with Baggage handling and 1 other fieldsHigh correlation
Baggage handling is highly correlated with On-board service and 1 other fieldsHigh correlation
Inflight service is highly correlated with On-board service and 1 other fieldsHigh correlation
Cleanliness is highly correlated with Food and drink and 2 other fieldsHigh correlation
Departure Delay in Minutes is highly correlated with Arrival Delay in MinutesHigh correlation
Arrival Delay in Minutes is highly correlated with Departure Delay in MinutesHigh correlation
Inflight wifi service is highly correlated with Ease of Online bookingHigh correlation
Ease of Online booking is highly correlated with Inflight wifi serviceHigh correlation
Food and drink is highly correlated with Inflight entertainment and 1 other fieldsHigh correlation
Seat comfort is highly correlated with Inflight entertainment and 1 other fieldsHigh correlation
Inflight entertainment is highly correlated with Food and drink and 2 other fieldsHigh correlation
On-board service is highly correlated with Inflight serviceHigh correlation
Baggage handling is highly correlated with Inflight serviceHigh correlation
Inflight service is highly correlated with On-board service and 1 other fieldsHigh correlation
Cleanliness is highly correlated with Food and drink and 2 other fieldsHigh correlation
Departure Delay in Minutes is highly correlated with Arrival Delay in MinutesHigh correlation
Arrival Delay in Minutes is highly correlated with Departure Delay in MinutesHigh correlation
Type of Travel is highly correlated with ClassHigh correlation
Class is highly correlated with Type of Travel and 1 other fieldsHigh correlation
satisfaction is highly correlated with ClassHigh correlation
Type of Travel is highly correlated with satisfactionHigh correlation
Class is highly correlated with Online boardingHigh correlation
Inflight wifi service is highly correlated with Departure/Arrival time convenient and 4 other fieldsHigh correlation
Departure/Arrival time convenient is highly correlated with Inflight wifi service and 2 other fieldsHigh correlation
Ease of Online booking is highly correlated with Inflight wifi service and 3 other fieldsHigh correlation
Gate location is highly correlated with Inflight wifi service and 2 other fieldsHigh correlation
Food and drink is highly correlated with Seat comfort and 2 other fieldsHigh correlation
Online boarding is highly correlated with Class and 5 other fieldsHigh correlation
Seat comfort is highly correlated with Food and drink and 5 other fieldsHigh correlation
Inflight entertainment is highly correlated with Food and drink and 6 other fieldsHigh correlation
On-board service is highly correlated with Inflight entertainment and 3 other fieldsHigh correlation
Leg room service is highly correlated with Inflight entertainment and 2 other fieldsHigh correlation
Baggage handling is highly correlated with On-board service and 1 other fieldsHigh correlation
Checkin service is highly correlated with Seat comfortHigh correlation
Inflight service is highly correlated with Inflight entertainment and 3 other fieldsHigh correlation
Cleanliness is highly correlated with Food and drink and 3 other fieldsHigh correlation
Departure Delay in Minutes is highly correlated with Arrival Delay in MinutesHigh correlation
Arrival Delay in Minutes is highly correlated with Departure Delay in MinutesHigh correlation
satisfaction is highly correlated with Type of Travel and 4 other fieldsHigh correlation
Unnamed: 0 is uniformly distributed Uniform
id is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
id has unique values Unique
Inflight wifi service has 3103 (3.0%) zeros Zeros
Departure/Arrival time convenient has 5300 (5.1%) zeros Zeros
Ease of Online booking has 4487 (4.3%) zeros Zeros
Online boarding has 2428 (2.3%) zeros Zeros
Departure Delay in Minutes has 58668 (56.5%) zeros Zeros
Arrival Delay in Minutes has 58159 (56.0%) zeros Zeros

Reproduction

Analysis started2021-11-24 03:48:14.878679
Analysis finished2021-11-24 03:49:38.223188
Duration1 minute and 23.34 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct103904
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51951.5
Minimum0
Maximum103903
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2021-11-23T20:49:38.297610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5195.15
Q125975.75
median51951.5
Q377927.25
95-th percentile98707.85
Maximum103903
Range103903
Interquartile range (IQR)51951.5

Descriptive statistics

Standard deviation29994.64552
Coefficient of variation (CV)0.5773586041
Kurtosis-1.2
Mean51951.5
Median Absolute Deviation (MAD)25976
Skewness0
Sum5397968656
Variance899678760
MonotonicityStrictly increasing
2021-11-23T20:49:38.462921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
299881
 
< 0.1%
811651
 
< 0.1%
750221
 
< 0.1%
770711
 
< 0.1%
1037041
 
< 0.1%
996101
 
< 0.1%
1016591
 
< 0.1%
217921
 
< 0.1%
238411
 
< 0.1%
Other values (103894)103894
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
1039031
< 0.1%
1039021
< 0.1%
1039011
< 0.1%
1039001
< 0.1%
1038991
< 0.1%
1038981
< 0.1%
1038971
< 0.1%
1038961
< 0.1%
1038951
< 0.1%
1038941
< 0.1%

id
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct103904
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64924.2105
Minimum1
Maximum129880
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2021-11-23T20:49:38.643236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6593.15
Q132533.75
median64856.5
Q397368.25
95-th percentile123409.7
Maximum129880
Range129879
Interquartile range (IQR)64834.5

Descriptive statistics

Standard deviation37463.81225
Coefficient of variation (CV)0.5770391655
Kurtosis-1.198440096
Mean64924.2105
Median Absolute Deviation (MAD)32410
Skewness0.002864248253
Sum6745885168
Variance1403537228
MonotonicityNot monotonic
2021-11-23T20:49:38.807104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40941
 
< 0.1%
830741
 
< 0.1%
522751
 
< 0.1%
625161
 
< 0.1%
645651
 
< 0.1%
584221
 
< 0.1%
604711
 
< 0.1%
338501
 
< 0.1%
358991
 
< 0.1%
461401
 
< 0.1%
Other values (103894)103894
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
1298801
< 0.1%
1298791
< 0.1%
1298781
< 0.1%
1298751
< 0.1%
1298741
< 0.1%
1298731
< 0.1%
1298711
< 0.1%
1298701
< 0.1%
1298691
< 0.1%
1298671
< 0.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size811.9 KiB
Female
52727 
Male
51177 

Length

Max length6
Median length6
Mean length5.014917616
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Female52727
50.7%
Male51177
49.3%

Length

2021-11-23T20:49:38.983025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-23T20:49:39.093503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
female52727
50.7%
male51177
49.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Customer Type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size811.9 KiB
Loyal Customer
84923 
disloyal Customer
18981 

Length

Max length17
Median length14
Mean length14.54803472
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLoyal Customer
2nd rowdisloyal Customer
3rd rowLoyal Customer
4th rowLoyal Customer
5th rowLoyal Customer

Common Values

ValueCountFrequency (%)
Loyal Customer84923
81.7%
disloyal Customer18981
 
18.3%

Length

2021-11-23T20:49:39.214959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-23T20:49:39.325945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
customer103904
50.0%
loyal84923
40.9%
disloyal18981
 
9.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Age
Real number (ℝ≥0)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.37970627
Minimum7
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2021-11-23T20:49:39.434005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14
Q127
median40
Q351
95-th percentile64
Maximum85
Range78
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.1149637
Coefficient of variation (CV)0.3838262174
Kurtosis-0.7195681169
Mean39.37970627
Median Absolute Deviation (MAD)12
Skewness-0.004516127072
Sum4091709
Variance228.4621276
MonotonicityNot monotonic
2021-11-23T20:49:39.582195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
392969
 
2.9%
252798
 
2.7%
402574
 
2.5%
442482
 
2.4%
422457
 
2.4%
412456
 
2.4%
222351
 
2.3%
232346
 
2.3%
452339
 
2.3%
472329
 
2.2%
Other values (65)78803
75.8%
ValueCountFrequency (%)
7562
0.5%
8640
0.6%
9692
0.7%
10683
0.7%
11678
0.7%
12635
0.6%
13633
0.6%
14707
0.7%
15818
0.8%
16899
0.9%
ValueCountFrequency (%)
8517
 
< 0.1%
8078
 
0.1%
7942
 
< 0.1%
7833
 
< 0.1%
7787
0.1%
7645
 
< 0.1%
7561
 
0.1%
7447
 
< 0.1%
7351
 
< 0.1%
72201
0.2%

Type of Travel
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size811.9 KiB
Business travel
71655 
Personal Travel
32249 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPersonal Travel
2nd rowBusiness travel
3rd rowBusiness travel
4th rowBusiness travel
5th rowBusiness travel

Common Values

ValueCountFrequency (%)
Business travel71655
69.0%
Personal Travel32249
31.0%

Length

2021-11-23T20:49:39.746748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-23T20:49:39.839719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
travel103904
50.0%
business71655
34.5%
personal32249
 
15.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Class
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size811.9 KiB
Business
49665 
Eco
46745 
Eco Plus
7494 

Length

Max length8
Median length8
Mean length5.750567832
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEco Plus
2nd rowBusiness
3rd rowBusiness
4th rowBusiness
5th rowBusiness

Common Values

ValueCountFrequency (%)
Business49665
47.8%
Eco46745
45.0%
Eco Plus7494
 
7.2%

Length

2021-11-23T20:49:39.930670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-23T20:49:40.031702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
eco54239
48.7%
business49665
44.6%
plus7494
 
6.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Flight Distance
Real number (ℝ≥0)

Distinct3802
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1189.448375
Minimum31
Maximum4983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2021-11-23T20:49:40.152345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile175
Q1414
median843
Q31743
95-th percentile3383
Maximum4983
Range4952
Interquartile range (IQR)1329

Descriptive statistics

Standard deviation997.1472805
Coefficient of variation (CV)0.838327498
Kurtosis0.2685354395
Mean1189.448375
Median Absolute Deviation (MAD)517
Skewness1.109465668
Sum123588444
Variance994302.6991
MonotonicityNot monotonic
2021-11-23T20:49:40.303960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
337660
 
0.6%
594395
 
0.4%
404392
 
0.4%
2475369
 
0.4%
862369
 
0.4%
447362
 
0.3%
236351
 
0.3%
192333
 
0.3%
399332
 
0.3%
308329
 
0.3%
Other values (3792)100012
96.3%
ValueCountFrequency (%)
318
 
< 0.1%
568
 
< 0.1%
67128
0.1%
7359
0.1%
7430
 
< 0.1%
761
 
< 0.1%
7741
 
< 0.1%
7830
 
< 0.1%
802
 
< 0.1%
827
 
< 0.1%
ValueCountFrequency (%)
498312
< 0.1%
496313
< 0.1%
48175
 
< 0.1%
450210
< 0.1%
424318
< 0.1%
400011
< 0.1%
39995
 
< 0.1%
39988
< 0.1%
39979
< 0.1%
39968
< 0.1%

Inflight wifi service
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.729683169
Minimum0
Maximum5
Zeros3103
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2021-11-23T20:49:40.445317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.327829471
Coefficient of variation (CV)0.4864408757
Kurtosis-0.8461697189
Mean2.729683169
Median Absolute Deviation (MAD)1
Skewness0.04040802158
Sum283625
Variance1.763131105
MonotonicityNot monotonic
2021-11-23T20:49:40.554421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
325868
24.9%
225830
24.9%
419794
19.1%
117840
17.2%
511469
11.0%
03103
 
3.0%
ValueCountFrequency (%)
03103
 
3.0%
117840
17.2%
225830
24.9%
325868
24.9%
419794
19.1%
511469
11.0%
ValueCountFrequency (%)
511469
11.0%
419794
19.1%
325868
24.9%
225830
24.9%
117840
17.2%
03103
 
3.0%

Departure/Arrival time convenient
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.060296043
Minimum0
Maximum5
Zeros5300
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2021-11-23T20:49:40.678313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.525075197
Coefficient of variation (CV)0.4983423748
Kurtosis-1.037767284
Mean3.060296043
Median Absolute Deviation (MAD)1
Skewness-0.3343986322
Sum317977
Variance2.325854357
MonotonicityNot monotonic
2021-11-23T20:49:40.780289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
425546
24.6%
522403
21.6%
317966
17.3%
217191
16.5%
115498
14.9%
05300
 
5.1%
ValueCountFrequency (%)
05300
 
5.1%
115498
14.9%
217191
16.5%
317966
17.3%
425546
24.6%
522403
21.6%
ValueCountFrequency (%)
522403
21.6%
425546
24.6%
317966
17.3%
217191
16.5%
115498
14.9%
05300
 
5.1%

Ease of Online booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.756900601
Minimum0
Maximum5
Zeros4487
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2021-11-23T20:49:40.909490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.398929473
Coefficient of variation (CV)0.5074283318
Kurtosis-0.9103462085
Mean2.756900601
Median Absolute Deviation (MAD)1
Skewness-0.01829427334
Sum286453
Variance1.957003669
MonotonicityNot monotonic
2021-11-23T20:49:41.144943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
324449
23.5%
224021
23.1%
419571
18.8%
117525
16.9%
513851
13.3%
04487
 
4.3%
ValueCountFrequency (%)
04487
 
4.3%
117525
16.9%
224021
23.1%
324449
23.5%
419571
18.8%
513851
13.3%
ValueCountFrequency (%)
513851
13.3%
419571
18.8%
324449
23.5%
224021
23.1%
117525
16.9%
04487
 
4.3%

Gate location
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.976882507
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2021-11-23T20:49:41.275599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.27762101
Coefficient of variation (CV)0.4291808653
Kurtosis-1.030283299
Mean2.976882507
Median Absolute Deviation (MAD)1
Skewness-0.05888941158
Sum309310
Variance1.632315446
MonotonicityNot monotonic
2021-11-23T20:49:41.387263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
328577
27.5%
424426
23.5%
219459
18.7%
117562
16.9%
513879
13.4%
01
 
< 0.1%
ValueCountFrequency (%)
01
 
< 0.1%
117562
16.9%
219459
18.7%
328577
27.5%
424426
23.5%
513879
13.4%
ValueCountFrequency (%)
513879
13.4%
424426
23.5%
328577
27.5%
219459
18.7%
117562
16.9%
01
 
< 0.1%

Food and drink
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.202128888
Minimum0
Maximum5
Zeros107
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2021-11-23T20:49:41.523898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.329532711
Coefficient of variation (CV)0.4152027471
Kurtosis-1.145453205
Mean3.202128888
Median Absolute Deviation (MAD)1
Skewness-0.151279497
Sum332714
Variance1.767657229
MonotonicityNot monotonic
2021-11-23T20:49:41.637625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
424359
23.4%
522313
21.5%
322300
21.5%
221988
21.2%
112837
12.4%
0107
 
0.1%
ValueCountFrequency (%)
0107
 
0.1%
112837
12.4%
221988
21.2%
322300
21.5%
424359
23.4%
522313
21.5%
ValueCountFrequency (%)
522313
21.5%
424359
23.4%
322300
21.5%
221988
21.2%
112837
12.4%
0107
 
0.1%

Online boarding
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.250375346
Minimum0
Maximum5
Zeros2428
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2021-11-23T20:49:41.757454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.349508954
Coefficient of variation (CV)0.4151855739
Kurtosis-0.7020058043
Mean3.250375346
Median Absolute Deviation (MAD)1
Skewness-0.4538516953
Sum337727
Variance1.821174416
MonotonicityNot monotonic
2021-11-23T20:49:41.876618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
430762
29.6%
321804
21.0%
520713
19.9%
217505
16.8%
110692
 
10.3%
02428
 
2.3%
ValueCountFrequency (%)
02428
 
2.3%
110692
 
10.3%
217505
16.8%
321804
21.0%
430762
29.6%
520713
19.9%
ValueCountFrequency (%)
520713
19.9%
430762
29.6%
321804
21.0%
217505
16.8%
110692
 
10.3%
02428
 
2.3%

Seat comfort
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.439395981
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2021-11-23T20:49:42.010343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.319087519
Coefficient of variation (CV)0.3835230157
Kurtosis-0.9257020682
Mean3.439395981
Median Absolute Deviation (MAD)1
Skewness-0.4827753882
Sum357367
Variance1.739991882
MonotonicityNot monotonic
2021-11-23T20:49:42.122376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
431765
30.6%
526470
25.5%
318696
18.0%
214897
14.3%
112075
 
11.6%
01
 
< 0.1%
ValueCountFrequency (%)
01
 
< 0.1%
112075
 
11.6%
214897
14.3%
318696
18.0%
431765
30.6%
526470
25.5%
ValueCountFrequency (%)
526470
25.5%
431765
30.6%
318696
18.0%
214897
14.3%
112075
 
11.6%
01
 
< 0.1%

Inflight entertainment
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.35815753
Minimum0
Maximum5
Zeros14
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2021-11-23T20:49:42.253026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.332990715
Coefficient of variation (CV)0.3969410913
Kurtosis-1.060695752
Mean3.35815753
Median Absolute Deviation (MAD)1
Skewness-0.3651305877
Sum348926
Variance1.776864245
MonotonicityNot monotonic
2021-11-23T20:49:42.364060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
429423
28.3%
525213
24.3%
319139
18.4%
217637
17.0%
112478
12.0%
014
 
< 0.1%
ValueCountFrequency (%)
014
 
< 0.1%
112478
12.0%
217637
17.0%
319139
18.4%
429423
28.3%
525213
24.3%
ValueCountFrequency (%)
525213
24.3%
429423
28.3%
319139
18.4%
217637
17.0%
112478
12.0%
014
 
< 0.1%

On-board service
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.382362565
Minimum0
Maximum5
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2021-11-23T20:49:42.494542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.288354361
Coefficient of variation (CV)0.3809036837
Kurtosis-0.8923352438
Mean3.382362565
Median Absolute Deviation (MAD)1
Skewness-0.4200307451
Sum351441
Variance1.659856959
MonotonicityNot monotonic
2021-11-23T20:49:42.613073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
430867
29.7%
523648
22.8%
322833
22.0%
214681
14.1%
111872
 
11.4%
03
 
< 0.1%
ValueCountFrequency (%)
03
 
< 0.1%
111872
 
11.4%
214681
14.1%
322833
22.0%
430867
29.7%
523648
22.8%
ValueCountFrequency (%)
523648
22.8%
430867
29.7%
322833
22.0%
214681
14.1%
111872
 
11.4%
03
 
< 0.1%

Leg room service
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.35105482
Minimum0
Maximum5
Zeros472
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2021-11-23T20:49:42.744528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.315604619
Coefficient of variation (CV)0.3925941801
Kurtosis-0.9802569111
Mean3.35105482
Median Absolute Deviation (MAD)1
Skewness-0.3502313446
Sum348188
Variance1.730815514
MonotonicityNot monotonic
2021-11-23T20:49:42.847415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
428789
27.7%
524667
23.7%
320098
19.3%
219525
18.8%
110353
 
10.0%
0472
 
0.5%
ValueCountFrequency (%)
0472
 
0.5%
110353
 
10.0%
219525
18.8%
320098
19.3%
428789
27.7%
524667
23.7%
ValueCountFrequency (%)
524667
23.7%
428789
27.7%
320098
19.3%
219525
18.8%
110353
 
10.0%
0472
 
0.5%

Baggage handling
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size811.9 KiB
4
37383 
5
27131 
3
20632 
2
11521 
1
7237 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row4
4th row3
5th row4

Common Values

ValueCountFrequency (%)
437383
36.0%
527131
26.1%
320632
19.9%
211521
 
11.1%
17237
 
7.0%

Length

2021-11-23T20:49:42.980176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-23T20:49:43.080127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
437383
36.0%
527131
26.1%
320632
19.9%
211521
 
11.1%
17237
 
7.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Checkin service
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.304290499
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2021-11-23T20:49:43.194547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.265395827
Coefficient of variation (CV)0.382955381
Kurtosis-0.8288770565
Mean3.304290499
Median Absolute Deviation (MAD)1
Skewness-0.3649819608
Sum343329
Variance1.601226599
MonotonicityNot monotonic
2021-11-23T20:49:43.306416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
429055
28.0%
328446
27.4%
520619
19.8%
212893
12.4%
112890
12.4%
01
 
< 0.1%
ValueCountFrequency (%)
01
 
< 0.1%
112890
12.4%
212893
12.4%
328446
27.4%
429055
28.0%
520619
19.8%
ValueCountFrequency (%)
520619
19.8%
429055
28.0%
328446
27.4%
212893
12.4%
112890
12.4%
01
 
< 0.1%

Inflight service
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.640427702
Minimum0
Maximum5
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2021-11-23T20:49:43.427589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.175663034
Coefficient of variation (CV)0.3229464035
Kurtosis-0.3575091976
Mean3.640427702
Median Absolute Deviation (MAD)1
Skewness-0.6903139573
Sum378255
Variance1.382183569
MonotonicityNot monotonic
2021-11-23T20:49:43.538707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
437945
36.5%
527116
26.1%
320299
19.5%
211457
 
11.0%
17084
 
6.8%
03
 
< 0.1%
ValueCountFrequency (%)
03
 
< 0.1%
17084
 
6.8%
211457
 
11.0%
320299
19.5%
437945
36.5%
527116
26.1%
ValueCountFrequency (%)
527116
26.1%
437945
36.5%
320299
19.5%
211457
 
11.0%
17084
 
6.8%
03
 
< 0.1%

Cleanliness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.286350862
Minimum0
Maximum5
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2021-11-23T20:49:43.669997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.312272847
Coefficient of variation (CV)0.3993100256
Kurtosis-1.012557651
Mean3.286350862
Median Absolute Deviation (MAD)1
Skewness-0.3000744927
Sum341465
Variance1.722060025
MonotonicityNot monotonic
2021-11-23T20:49:43.771063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
427179
26.2%
324574
23.7%
522689
21.8%
216132
15.5%
113318
12.8%
012
 
< 0.1%
ValueCountFrequency (%)
012
 
< 0.1%
113318
12.8%
216132
15.5%
324574
23.7%
427179
26.2%
522689
21.8%
ValueCountFrequency (%)
522689
21.8%
427179
26.2%
324574
23.7%
216132
15.5%
113318
12.8%
012
 
< 0.1%

Departure Delay in Minutes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct446
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.81561826
Minimum0
Maximum1592
Zeros58668
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2021-11-23T20:49:43.922317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile78
Maximum1592
Range1592
Interquartile range (IQR)12

Descriptive statistics

Standard deviation38.23090058
Coefficient of variation (CV)2.580445845
Kurtosis100.2670058
Mean14.81561826
Median Absolute Deviation (MAD)0
Skewness6.73397951
Sum1539402
Variance1461.601759
MonotonicityNot monotonic
2021-11-23T20:49:44.071756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
058668
56.5%
12948
 
2.8%
22274
 
2.2%
32009
 
1.9%
41854
 
1.8%
51692
 
1.6%
61517
 
1.5%
71392
 
1.3%
81295
 
1.2%
91255
 
1.2%
Other values (436)29000
27.9%
ValueCountFrequency (%)
058668
56.5%
12948
 
2.8%
22274
 
2.2%
32009
 
1.9%
41854
 
1.8%
51692
 
1.6%
61517
 
1.5%
71392
 
1.3%
81295
 
1.2%
91255
 
1.2%
ValueCountFrequency (%)
15921
< 0.1%
13051
< 0.1%
10171
< 0.1%
9781
< 0.1%
9331
< 0.1%
9301
< 0.1%
9211
< 0.1%
8591
< 0.1%
8531
< 0.1%
7501
< 0.1%

Arrival Delay in Minutes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct455
Distinct (%)0.4%
Missing310
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean15.1786783
Minimum0
Maximum1584
Zeros58159
Zeros (%)56.0%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2021-11-23T20:49:44.245666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q313
95-th percentile79
Maximum1584
Range1584
Interquartile range (IQR)13

Descriptive statistics

Standard deviation38.69868202
Coefficient of variation (CV)2.549542276
Kurtosis94.5370055
Mean15.1786783
Median Absolute Deviation (MAD)0
Skewness6.596636807
Sum1572420
Variance1497.58799
MonotonicityNot monotonic
2021-11-23T20:49:44.398674image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
058159
56.0%
12211
 
2.1%
22064
 
2.0%
31952
 
1.9%
41907
 
1.8%
51658
 
1.6%
61616
 
1.6%
71481
 
1.4%
81394
 
1.3%
91264
 
1.2%
Other values (445)29888
28.8%
ValueCountFrequency (%)
058159
56.0%
12211
 
2.1%
22064
 
2.0%
31952
 
1.9%
41907
 
1.8%
51658
 
1.6%
61616
 
1.6%
71481
 
1.4%
81394
 
1.3%
91264
 
1.2%
ValueCountFrequency (%)
15841
< 0.1%
12801
< 0.1%
10111
< 0.1%
9701
< 0.1%
9521
< 0.1%
9241
< 0.1%
9201
< 0.1%
8601
< 0.1%
8231
< 0.1%
7291
< 0.1%

satisfaction
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size811.9 KiB
neutral or dissatisfied
58879 
satisfied
45025 

Length

Max length23
Median length23
Mean length16.93334232
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowneutral or dissatisfied
2nd rowneutral or dissatisfied
3rd rowsatisfied
4th rowneutral or dissatisfied
5th rowsatisfied

Common Values

ValueCountFrequency (%)
neutral or dissatisfied58879
56.7%
satisfied45025
43.3%

Length

2021-11-23T20:49:44.682941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-23T20:49:44.785041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
dissatisfied58879
26.6%
or58879
26.6%
neutral58879
26.6%
satisfied45025
20.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-11-23T20:49:32.466904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:27.987029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:32.319442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:35.765264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:39.552660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:43.065546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:46.845984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:50.629477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:54.251797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:57.880166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:01.487908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:05.027102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:08.454215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:11.778919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:15.237623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:18.542231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:21.943481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:25.412403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:28.812782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:32.648564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:28.218702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:32.493977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:35.942799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:39.731230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:43.251052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:47.036705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:50.826993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:54.446311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:58.060158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:01.660038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:05.200209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:08.623407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:11.959847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:15.418958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:18.715886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:22.115955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:25.583866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:28.994491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:32.834187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:28.400579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:32.683515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:36.151754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:39.940668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:43.567182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:47.227258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:51.033438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:54.638036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:58.246060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:01.853503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:05.372492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:08.801867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:12.132797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:15.594567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:18.887182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:22.289047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:25.759627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:29.180377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:33.028226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:28.591330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:32.869021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:36.364234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:40.133155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:43.756709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:47.415801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:51.282738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:54.816980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:58.430170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:02.045603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:05.567602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:08.984957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:12.315234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:15.779527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:19.072839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:22.470727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:25.950306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:29.367225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:33.201830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:28.764136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:33.044538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:36.559665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:40.326590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:43.939425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:47.591239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:51.489186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:54.995204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:58.634416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:02.349448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:05.742877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:09.148814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:12.486302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:15.947338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:19.244936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:22.635838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:26.115910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:29.552111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:33.384307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:28.935727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:33.221032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:36.743180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:40.500160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:44.123886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:47.786901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:51.679016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:55.154416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:58.862803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:02.531111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:05.910885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:09.331167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:12.649882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:16.119141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:19.417088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:22.807286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:26.289475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:29.735591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:33.568101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:29.112470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:33.411568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:36.921729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:40.672698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:44.352323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:47.973356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:51.867662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:55.351280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:59.087206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:02.695787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:06.092858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:09.512465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:12.825164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:16.290154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:19.711884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:22.980959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:26.452920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:29.929147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:33.751634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:29.298976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:33.586102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:37.097274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:40.846247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:44.541815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:48.143627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:52.040408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:55.556875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:59.313620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:02.880603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:06.262856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:09.697523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:13.003571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:16.463624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:19.874816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:23.146211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:26.626566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:30.111969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:33.935505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:29.474589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:33.758639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:37.395463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:41.030709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:44.784783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:48.315323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:52.228204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:55.744095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:59.504090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:03.063732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:06.428363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:09.860307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:13.172917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:16.637546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:20.046431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:23.318290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:26.789578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:30.304405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:34.119638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:29.650905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:33.931178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:37.575993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:41.214504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:45.010181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:48.496947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:52.413678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:55.915868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:59.695576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:03.243961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:06.591691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:10.029671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:13.342283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:16.809544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:20.211636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:23.491864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:26.990606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:30.477968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:34.303617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:29.823343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:34.104715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:37.753505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:41.387046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:45.225604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:48.680247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:52.585346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:56.216718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:59.900740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:03.416445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:06.764734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:10.199037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:13.504961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:16.981797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:20.385176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:23.655368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:27.175046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:30.661606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:34.485375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:29.998258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:34.277253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:37.931995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:41.570545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:45.415099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:48.864765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:52.767832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:56.393828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:00.074986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:03.574727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:06.945802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:10.375382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:13.674450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:17.153826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:20.548113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:23.843936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:27.391150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:30.845325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:34.669654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:30.175189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:34.450790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:38.135450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:41.761033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:45.590860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:49.048206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:52.948865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:56.570403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:00.248717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:03.763713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:07.109840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:10.547103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:13.968390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:17.325485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:20.720889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:24.028388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:27.560540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:31.028832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:34.852016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:30.358697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:34.625323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:38.372847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:41.940554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:45.764473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:49.225998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:53.133268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:56.770860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:00.418021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:03.936881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:07.281044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:10.714980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:14.133676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:17.490862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:20.894238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:24.201669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:27.738955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:31.325339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:35.035906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:31.418658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:34.803811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:38.568338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:42.114090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:45.936121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:49.415843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:53.306697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:56.966129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:00.596137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:04.108608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:07.445066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:10.900728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:14.315607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:17.662352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:21.057749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:24.403788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:27.923710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:31.509318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:35.224641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:31.596193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:34.976338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:38.757818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:42.285629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:46.100748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:49.652211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:53.489364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:57.158511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:00.765807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:04.288439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:07.625500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:11.064350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:14.470481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:17.827807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:21.231006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:24.575051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:28.093048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:31.692733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:35.401932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:31.765738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:35.153864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:38.949275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:42.477085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:46.278030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:49.956154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:53.662675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:57.323168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:00.939226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:04.471334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:07.790605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:11.230693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:14.658732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:17.999287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:21.401901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:24.740253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:28.262281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:31.874130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:35.586893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:31.936268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:35.328397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:39.139812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:42.676600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:46.443622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:50.174292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:53.834543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:57.494997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:01.110245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:04.643212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:07.962473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:11.409098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:14.832827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:18.164686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:21.567239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:24.913398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:28.443435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:32.057744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:35.791123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:32.130981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:35.534890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:39.347209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:42.873027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:46.662701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:50.415051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:54.036849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:48:57.686973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:01.304697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:04.829000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:08.269243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:11.594023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:15.058219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:18.356469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:21.744395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:25.106775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:28.629248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-23T20:49:32.253979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-11-23T20:49:44.889261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-23T20:49:45.262601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-23T20:49:45.616204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-23T20:49:45.978279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-11-23T20:49:46.193738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-23T20:49:36.106620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-23T20:49:37.249559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-11-23T20:49:37.889732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Unnamed: 0idGenderCustomer TypeAgeType of TravelClassFlight DistanceInflight wifi serviceDeparture/Arrival time convenientEase of Online bookingGate locationFood and drinkOnline boardingSeat comfortInflight entertainmentOn-board serviceLeg room serviceBaggage handlingCheckin serviceInflight serviceCleanlinessDeparture Delay in MinutesArrival Delay in Minutessatisfaction
0070172MaleLoyal Customer13Personal TravelEco Plus460343153554344552518.0neutral or dissatisfied
115047Maledisloyal Customer25Business travelBusiness2353233131115314116.0neutral or dissatisfied
22110028FemaleLoyal Customer26Business travelBusiness11422222555543444500.0satisfied
3324026FemaleLoyal Customer25Business travelBusiness56225552222253142119.0neutral or dissatisfied
44119299MaleLoyal Customer61Business travelBusiness2143333455334433300.0satisfied
55111157FemaleLoyal Customer26Personal TravelEco11803421121134444100.0neutral or dissatisfied
6682113MaleLoyal Customer47Personal TravelEco127624232222334352923.0neutral or dissatisfied
7796462FemaleLoyal Customer52Business travelBusiness20354344555555545440.0satisfied
8879485FemaleLoyal Customer41Business travelBusiness8531222433112141200.0neutral or dissatisfied
9965725Maledisloyal Customer20Business travelEco10613334233223443200.0neutral or dissatisfied

Last rows

Unnamed: 0idGenderCustomer TypeAgeType of TravelClassFlight DistanceInflight wifi serviceDeparture/Arrival time convenientEase of Online bookingGate locationFood and drinkOnline boardingSeat comfortInflight entertainmentOn-board serviceLeg room serviceBaggage handlingCheckin serviceInflight serviceCleanlinessDeparture Delay in MinutesArrival Delay in Minutessatisfaction
10389410389486549MaleLoyal Customer26Business travelBusiness712444455553443451726.0satisfied
10389510389566030Femaledisloyal Customer24Business travelEco1055111211113355411310.0neutral or dissatisfied
10389610389671445MaleLoyal Customer57Business travelEco8674555444434313400.0neutral or dissatisfied
103897103897102203FemaleLoyal Customer60Business travelBusiness15995555554444444497.0satisfied
10389810389860666MaleLoyal Customer50Personal TravelEco16203134232243424200.0neutral or dissatisfied
10389910389994171Femaledisloyal Customer23Business travelEco1922123222231423230.0neutral or dissatisfied
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